Enhancing Customer Retention in Banking: Integrating AI Churn Prediction with CRM Automation

Abstract

In the banking industry, where aggressive competition from digital banks poses a growing threat, customer retention is no longer just a business strategy -it's a prerequisite for long-term profitability. This study unveils a revolutionary approach to mitigate customer churn through the synergy of AI-based churn prediction and CRM automation. By analyzing historical transaction data from Bank XYZ, we developed a machine learning model that not only accurately identifies high-risk customers but also automatically triggers personalized retention interventions. Through a rigorous A/B test experiment, we proved that this proactive approach results in a phenomenal reduction in churn rates. The study's findings show a proactive intervention success rate of 73.68%, a figure that significantly surpasses conventional retention methods. This finding not only solidifies the vital role of AI in business decision-making but also paves a new path for the banking industry to build efficient, proactive, and sustainable retention strategies in the digital era.

Keywords: Customer Churn, Machine Learning, Customer Retention, CRM Automation